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arxiv: 2606.23533 · v1 · pith:3MLSH7JVnew · submitted 2026-06-22 · 💻 cs.AI

POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation

Hung Phan , Aniroop Naladala , Dubey Avanindra , Supryia Chinthavali , Lunga Dalton , Ali Jannesari This is my paper

Pith reviewed 2026-06-26 08:35 UTC · model grok-4.3

classification 💻 cs.AI
keywords power outage reportsLLM fine-tuningPOTrackerLossstructural similarityenergy sector standardsmachine-readable reportsdomain-specific generationregulatory compliance
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The pith

POTracker fine-tunes an LLM using a loss that matches both text and report tags to reach 86.47 percent structural accuracy on power outage reports.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to demonstrate that large language models can be made to generate domain-specific outputs that satisfy strict formatting rules by training them with an objective that penalizes both wording errors and structural mismatches. This would matter if true because utility companies currently struggle to produce machine-readable power outage reports that comply with national energy regulations without heavy manual intervention. The authors fine-tune Qwen2.5-7B-Instruct on one thousand existing reports and introduce POTrackerLoss to enforce similarity in both content and tag structure. Evaluation against other fine-tuning techniques and a rule-based baseline shows clear gains, with a separate human review rating the source reports at 4.03 out of 5.

Core claim

The central claim is that POTrackerLoss, which adds structural tag similarity to ordinary textual similarity during fine-tuning, enables the model to generate power outage reports that are both semantically accurate and compliant with regulatory standards, outperforming five other fine-tuning methods by up to 51 percent overall accuracy while reaching 86.47 percent structural accuracy on a dataset of one thousand reports.

What carries the argument

POTrackerLoss, a training objective that sums textual similarity with structural tag similarity between the generated report and the ground-truth report.

If this is right

  • Power utilities could automate the creation of machine-readable outage reports that already satisfy regulatory formatting without manual post-processing.
  • The same training approach would reduce the rate at which generated reports need correction for missing or incorrect tags.
  • Fine-tuning with explicit structural penalties would generalize better than standard text-only objectives when outputs must follow fixed schemas.
  • Human-rated ground-truth data at 4.03 out of 5 indicates the training set itself meets expert standards for use in further model development.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The loss design could transfer to other regulated document types that require both narrative accuracy and rigid tag structures, such as medical discharge summaries.
  • Deploying the model in real-time outage management systems would let utilities produce compliant reports during active events rather than after the fact.
  • Future work could test whether adding explicit regulatory-rule constraints to the loss produces even higher compliance rates than tag similarity alone.

Load-bearing premise

The one thousand power outage reports used for training and testing are representative of all U.S. utility standards, and the combined loss is enough by itself to guarantee full compliance.

What would settle it

Generate reports from the model on a fresh collection of outage data drawn from a different utility region and have independent regulators score whether the outputs satisfy the required machine-readable format and content rules.

Figures

Figures reproduced from arXiv: 2606.23533 by Ali Jannesari, Aniroop Naladala, Dubey Avanindra, Hung Phan, Lunga Dalton, Supryia Chinthavali.

Figure 1
Figure 1. Figure 1: Examples of inputs and output of POTracker [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Architecture Overview of POTracker. data each year. To address this, we employ closed LLMs guided by tailored prompts to generate large￾scale standard reports from nonstandard inputs. Closed LLMs, which require access fees and API tokens, are known for their high-quality generative capabilities. In our project, We prompt GPT-5.2 to convert nonstandard reports into standard reports based on the requirements… view at source ↗
read the original abstract

Recent large language models (LLMs) are good at general text generation, but it is still hard to use them for domain-specific data generation because the output must follow strict formatting and structural rules. Unlike open-ended tasks such as question answering or translation, domain-specific generation must be both semantically correct and compliant with existing guidelines and standards. In this work, we study the nationwide interoperability problem of utility power outage reports in the United States. In practice, outage reports need to be machine-readable (e.g., JSON or XML) and must strictly follow requirements from energy-sector regulatory bodies. To address this problem, we propose POTracker, an optimized LLM for power outage report generation. We fine-tune Qwen2.5-7B-Instruct using our proposed objective. The key contribution is a new loss function, POTrackerLoss, that considers both textual similarity and structural (tag) similarity between the generated report and the ground-truth report. We evaluate POTracker on a dataset of 1,000 power outage reports and compare it with five well-known fine-tuning methods and one rule-based XML conversion method. Results show that POTracker outperforms other fine-tuning approaches, improving overall accuracy by up to 51% and reaching 86.47% structural accuracy for generated power outage reports. In addition, we conduct a human study to assess the quality of the ground-truth standard reports, where domain experts assign the generated labels an average score of 4.03 on a 0--5 scale.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes POTracker, a fine-tuned Qwen2.5-7B-Instruct model using a novel POTrackerLoss that combines textual similarity and structural (tag) similarity. Evaluated on a dataset of 1,000 power outage reports against five fine-tuning baselines and one rule-based method, it claims up to 51% improvement in overall accuracy and 86.47% structural accuracy, supported by a human study in which domain experts rate outputs at 4.03/5 on a 0-5 scale.

Significance. If the quantitative claims hold after full methodological disclosure, the work would provide a concrete example of loss-augmented fine-tuning for generating machine-readable outputs under regulatory constraints in the energy sector. The dual textual-structural loss could be reusable for other structured generation tasks. The human study adds a useful qualitative dimension, but the absence of loss definitions, training details, and constraint-validation procedures limits immediate assessment of broader impact or reproducibility.

major comments (2)
  1. [Abstract] Abstract: the central claims of up to 51% accuracy improvement and 86.47% structural accuracy cannot be evaluated because the abstract supplies neither the formula for POTrackerLoss nor any description of how structural accuracy is computed, what the five baseline fine-tuning methods are, or whether statistical significance or error bars were used.
  2. [Abstract] Abstract: the evaluation assumes that textual-plus-tag similarity is sufficient to guarantee full regulatory compliance, yet the abstract provides no evidence of explicit checks for field-specific formats, value ranges, inter-field logical consistency, required vs. optional elements, or machine-parseability rules that are standard in utility reporting.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments on the abstract below and will revise the manuscript accordingly to improve self-containment and clarity of the claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claims of up to 51% accuracy improvement and 86.47% structural accuracy cannot be evaluated because the abstract supplies neither the formula for POTrackerLoss nor any description of how structural accuracy is computed, what the five baseline fine-tuning methods are, or whether statistical significance or error bars were used.

    Authors: We agree the abstract should be more self-contained for evaluating the central claims. The full manuscript provides the POTrackerLoss formula (Section 3), structural accuracy definition (Section 4.2), descriptions of the five fine-tuning baselines (Section 4.1), and reports statistical significance with error bars in the results tables. We will revise the abstract to include concise descriptions of POTrackerLoss, the baselines, and a note on the statistical analysis. revision: yes

  2. Referee: [Abstract] Abstract: the evaluation assumes that textual-plus-tag similarity is sufficient to guarantee full regulatory compliance, yet the abstract provides no evidence of explicit checks for field-specific formats, value ranges, inter-field logical consistency, required vs. optional elements, or machine-parseability rules that are standard in utility reporting.

    Authors: POTrackerLoss is designed to jointly optimize textual and structural (tag) similarity precisely to enforce the regulatory formatting and structural rules for power outage reports. The 86.47% structural accuracy and domain-expert human ratings provide supporting evidence. We acknowledge the abstract does not explicitly list additional post-generation validation steps; we will revise it to briefly summarize the constraint-validation procedures (field formats, ranges, logical consistency, and parseability) that are detailed in the evaluation section of the full manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical fine-tuning and held-out evaluation with no derivations or self-referential reductions

full rationale

The paper describes a standard empirical ML workflow: fine-tuning Qwen2.5-7B-Instruct with a proposed POTrackerLoss (textual + structural similarity) and reporting accuracy gains on a 1,000-report dataset versus baselines. No equations, derivations, uniqueness theorems, or self-citations are invoked as load-bearing steps in the abstract or described claims. Results are direct performance comparisons on held-out data, not reductions of outputs to inputs by construction. The skeptic concern about regulatory constraints is a correctness issue, not a circularity issue.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no information on free parameters, background axioms, or new postulated entities; the method is described as standard fine-tuning augmented by a custom loss whose internal definition is not supplied.

pith-pipeline@v0.9.1-grok · 5823 in / 1288 out tokens · 33743 ms · 2026-06-26T08:35:04.231835+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

54 extracted references · 6 canonical work pages

  1. [1]

    Large Language Models for Automated Data Science: Introducing

    Noah Hollmann and Samuel M. Large Language Models for Automated Data Science: Introducing. Thirty-seventh Conference on Neural Information Processing Systems , year=

  2. [2]

    2024 , eprint=

    Examining Long-Context Large Language Models for Environmental Review Document Comprehension , author=. 2024 , eprint=

    2024

  3. [3]

    2024 , eprint=

    Data Science with LLMs and Interpretable Models , author=. 2024 , eprint=

    2024

  4. [4]

    Bzdok, A

    Danilo Bzdok and Andrew Thieme and Oleksiy Levkovskyy and Paul Wren and Thomas Ray and Siva Reddy , abstract =. Data science opportunities of large language models for neuroscience and biomedicine , journal =. 2024 , issn =. doi:https://doi.org/10.1016/j.neuron.2024.01.016 , url =

    work page doi:10.1016/j.neuron.2024.01.016 2024

  5. [5]

    2024 , eprint=

    LLM4DS: Evaluating Large Language Models for Data Science Code Generation , author=. 2024 , eprint=

    2024

  6. [6]

    2023 , eprint=

    Benchmarking Large Language Models for News Summarization , author=. 2023 , eprint=

    2023

  7. [7]

    2023 , eprint=

    Evaluating Open-Domain Question Answering in the Era of Large Language Models , author=. 2023 , eprint=

    2023

  8. [8]

    2024 , eprint=

    Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis , author=. 2024 , eprint=

    2024

  9. [9]

    2024 , eprint=

    Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey , author=. 2024 , eprint=

    2024

  10. [10]

    2024 , eprint=

    GPT-4 Technical Report , author=. 2024 , eprint=

    2024

  11. [11]

    2024 , howpublished =

    The Claude 3 Model Family: Opus, Sonnet, Haiku , author =. 2024 , howpublished =

    2024

  12. [12]

    2023 , eprint=

    Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering , author=. 2023 , eprint=

    2023

  13. [13]

    2025 , eprint=

    AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs , author=. 2025 , eprint=

    2025

  14. [14]

    2023 , howpublished =

    Outage Data Initiative Nationwide (ODIN) , author =. 2023 , howpublished =

    2023

  15. [15]

    2023 , url =

    Oak Ridge National Laboratory , title =. 2023 , url =

    2023

  16. [16]

    2024 , journal =

    SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts , author =. 2024 , journal =

    2024

  17. [17]

    History of Programming Languages—II , year =

    A History of SQL , author =. History of Programming Languages—II , year =

  18. [18]

    2025 , eprint=

    From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems , author=. 2025 , eprint=

    2025

  19. [19]

    2024 , url =

    Hugging Face , title =. 2024 , url =

    2024

  20. [20]

    2024 , howpublished =

    HumanSignal , title =. 2024 , howpublished =

    2024

  21. [21]

    2024 , eprint=

    Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach , author=. 2024 , eprint=

    2024

  22. [22]

    Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) , year =

    BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , author =. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) , year =

    2019

  23. [23]

    Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP) , year =

    CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation , author =. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP) , year =

    2021

  24. [24]

    2024 , eprint=

    The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities , author=. 2024 , eprint=

    2024

  25. [25]

    2024 , eprint=

    Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning , author=. 2024 , eprint=

    2024

  26. [26]

    Cognitive Science , volume =

    Finding structure in time , author =. Cognitive Science , volume =. 1990 , publisher =

    1990

  27. [27]

    2024 , eprint =

    A Technical Note on the Architectural Effects on Maximum Dependency Lengths of Recurrent Neural Networks , author =. 2024 , eprint =

    2024

  28. [28]

    arXiv preprint arXiv:2305.18290 , year =

    Direct Preference Optimization: Your Language Model is Secretly a Reward Model , author =. arXiv preprint arXiv:2305.18290 , year =

    Pith/arXiv arXiv

  29. [29]

    2010 , isbn =

    Preference Learning , author =. 2010 , isbn =

    2010

  30. [30]

    2024 , month=

    Enhancing XML-based Compiler Construction with Large Language Models: A Novel Approach , volume=. 2024 , month=. doi:10.58496/MJBD/2024/003 , journal=

    work page doi:10.58496/mjbd/2024/003 2024

  31. [31]

    2023 , note =

    Christoph Hack , title =. 2023 , note =

    2023

  32. [32]

    arXiv preprint arXiv:2302.13971 , year =

    LLaMA: Open and Efficient Foundation Language Models , author =. arXiv preprint arXiv:2302.13971 , year =

    Pith/arXiv arXiv

  33. [33]

    arXiv preprint arXiv:2203.02155 , year =

    Training language models to follow instructions with human feedback , author =. arXiv preprint arXiv:2203.02155 , year =

    Pith/arXiv arXiv

  34. [34]

    arXiv preprint arXiv:2309.13078 , year =

    LPML: LLM-Prompting Markup Language for Mathematical Reasoning , author =. arXiv preprint arXiv:2309.13078 , year =

    arXiv

  35. [35]

    arXiv preprint arXiv:2110.08518 , year =

    MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding , author =. arXiv preprint arXiv:2110.08518 , year =

    arXiv

  36. [36]

    2025 , month = dec, url =

    Introducing. 2025 , month = dec, url =

    2025

  37. [37]

    2008 , publisher =

    Introduction to Information Retrieval , author =. 2008 , publisher =

    2008

  38. [38]

    difflib --- Helpers for computing deltas , author =

  39. [39]

    International Conference on Learning Representations (ICLR) , year =

    Finetuned Language Models are Zero-Shot Learners , author =. International Conference on Learning Representations (ICLR) , year =

  40. [40]

    Parameter-Efficient Transfer Learning for

    Houlsby, Neil and Giurgiu, Andrei and Jastrzebski, Stanislaw and Morrone, Bruna and De Laroussilhe, Quentin and Gesmundo, Andrea and Attariyan, Mona and Gelly, Sylvain , booktitle =. Parameter-Efficient Transfer Learning for. 2019 , volume =

    2019

  41. [41]

    arXiv preprint arXiv:2106.09685 , year =

    LoRA: Low-Rank Adaptation of Large Language Models , author =. arXiv preprint arXiv:2106.09685 , year =

    Pith/arXiv arXiv

  42. [42]

    2018 , eprint=

    Focal Loss for Dense Object Detection , author=. 2018 , eprint=

    2018

  43. [43]

    2019 , eprint=

    BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , author=. 2019 , eprint=

    2019

  44. [44]

    2020 , eprint=

    SpanBERT: Improving Pre-training by Representing and Predicting Spans , author=. 2020 , eprint=

    2020

  45. [45]

    2019 , eprint=

    Learning to Reweight Examples for Robust Deep Learning , author=. 2019 , eprint=

    2019

  46. [46]

    2019 , eprint=

    Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting , author=. 2019 , eprint=

    2019

  47. [47]

    Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , year =

    Minimum Risk Training for Neural Machine Translation , author =. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , year =. doi:10.18653/v1/P16-1159 , url =

    work page doi:10.18653/v1/p16-1159

  48. [48]

    B leu: a Method for Automatic Evaluation of Machine Translation

    Bleu: a Method for Automatic Evaluation of Machine Translation , author =. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics , year =. doi:10.3115/1073083.1073135 , url =

    work page doi:10.3115/1073083.1073135

  49. [49]

    Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics , year =

    Minimum Error Rate Training in Statistical Machine Translation , author =. Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics , year =. doi:10.3115/1075096.1075117 , url =

    work page doi:10.3115/1075096.1075117

  50. [50]

    Rule-Based Generation of XML DTDs from UML Class Diagrams

    Kudrass, Thomas and Krumbein, Tobias. Rule-Based Generation of XML DTDs from UML Class Diagrams. Advances in Databases and Information Systems. 2003

    2003

  51. [51]

    Proceedings of the 9th International Conference on Practical Aspects of Declarative Languages , pages =

    Berdaguer, Pablo and Cunha, Alcino and Pacheco, Hugo and Visser, Joost , title =. Proceedings of the 9th International Conference on Practical Aspects of Declarative Languages , pages =. 2007 , isbn =. doi:10.1007/978-3-540-69611-7_19 , abstract =

    work page doi:10.1007/978-3-540-69611-7_19 2007

  52. [52]

    2025 , eprint =

    Qwen2.5 Technical Report , author =. 2025 , eprint =

    2025

  53. [53]

    2024 , howpublished =

    Qwen/Qwen2.5-7B-Instruct (Model Card) , author =. 2024 , howpublished =

    2024

  54. [54]

    2024 , howpublished =

    Qwen2.5: A Party of Foundation Models! , author =. 2024 , howpublished =

    2024